id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.06720 | Multi-Label Scene Classification in Remote Sensing Benefits from Image
Super-Resolution | [
"cs.CV",
"cs.AI"
] | Satellite imagery is a cornerstone for numerous Remote Sensing (RS) applications; however, limited spatial resolution frequently hinders the precision of such systems, especially in multi-label scene classification tasks as it requires a higher level of detail and feature differentiation. In this study, we explore the ... | {
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2501.06721 | On the effect of the average clustering coefficient on topology-based
link prediction in featureless graphs | [
"cs.SI"
] | Link prediction is a fundamental problem in graph theory with diverse applications, including recommender systems, community detection, and identifying spurious connections. While feature-based methods achieve high accuracy, their reliance on node attributes limits their applicability in featureless graphs. For such gr... | {
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2501.06724 | Wavelet Integrated Convolutional Neural Network for ECG Signal Denoising | [
"eess.SP",
"cs.CV"
] | Wearable electrocardiogram (ECG) measurement using dry electrodes has a problem with high-intensity noise distortion. Hence, a robust noise reduction method is required. However, overlapping frequency bands of ECG and noise make noise reduction difficult. Hence, it is necessary to provide a mechanism that changes the c... | {
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2501.06726 | Integrated Sensing and Edge AI: Realizing Intelligent Perception in 6G | [
"cs.IT",
"eess.SP",
"math.IT"
] | Sensing and edge artificial intelligence (AI) are envisioned as two essential and interconnected functions in sixth-generation (6G) mobile networks. On the one hand, sensing-empowered applications rely on powerful AI models to extract features and understand semantics from ubiquitous wireless sensors. On the other hand... | {
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2501.06728 | Measuring the Robustness of Reference-Free Dialogue Evaluation Systems | [
"cs.CL"
] | Advancements in dialogue systems powered by large language models (LLMs) have outpaced the development of reliable evaluation metrics, particularly for diverse and creative responses. We present a benchmark for evaluating the robustness of reference-free dialogue metrics against four categories of adversarial attacks: ... | {
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2501.06730 | Better Prompt Compression Without Multi-Layer Perceptrons | [
"cs.CL",
"cs.LG"
] | Prompt compression is a promising approach to speeding up language model inference without altering the generative model. Prior works compress prompts into smaller sequences of learned tokens using an encoder that is trained as a LowRank Adaptation (LoRA) of the inference language model. However, we show that the encod... | {
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2501.06736 | ZOQO: Zero-Order Quantized Optimization | [
"cs.LG",
"cs.CL"
] | The increasing computational and memory demands in deep learning present significant challenges, especially in resource-constrained environments. We introduce a zero-order quantized optimization (ZOQO) method designed for training models with quantized parameters and operations. Our approach leverages zero-order approx... | {
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2501.06740 | Rice Leaf Disease Detection: A Comparative Study Between CNN,
Transformer and Non-neural Network Architectures | [
"cs.CV"
] | In nations such as Bangladesh, agriculture plays a vital role in providing livelihoods for a significant portion of the population. Identifying and classifying plant diseases early is critical to prevent their spread and minimize their impact on crop yield and quality. Various computer vision techniques can be used for... | {
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2501.06741 | Hierarchical Divide-and-Conquer for Fine-Grained Alignment in LLM-Based
Medical Evaluation | [
"cs.CL"
] | In the rapidly evolving landscape of large language models (LLMs) for medical applications, ensuring the reliability and accuracy of these models in clinical settings is paramount. Existing benchmarks often focus on fixed-format tasks like multiple-choice QA, which fail to capture the complexity of real-world clinical ... | {
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2501.06746 | Diversified Augmentation with Domain Adaptation for Debiased Video
Temporal Grounding | [
"cs.CV"
] | Temporal sentence grounding in videos (TSGV) faces challenges due to public TSGV datasets containing significant temporal biases, which are attributed to the uneven temporal distributions of target moments. Existing methods generate augmented videos, where target moments are forced to have varying temporal locations. H... | {
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2501.06749 | Static Segmentation by Tracking: A Frustratingly Label-Efficient
Approach to Fine-Grained Segmentation | [
"cs.CV",
"cs.AI"
] | We study image segmentation in the biological domain, particularly trait and part segmentation from specimen images (e.g., butterfly wing stripes or beetle body parts). This is a crucial, fine-grained task that aids in understanding the biology of organisms. The conventional approach involves hand-labeling masks, often... | {
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2501.06751 | Padding Tone: A Mechanistic Analysis of Padding Tokens in T2I Models | [
"cs.CL",
"cs.CV"
] | Text-to-image (T2I) diffusion models rely on encoded prompts to guide the image generation process. Typically, these prompts are extended to a fixed length by adding padding tokens before text encoding. Despite being a default practice, the influence of padding tokens on the image generation process has not been invest... | {
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2501.06753 | Procedural Fairness and Its Relationship with Distributive Fairness in
Machine Learning | [
"cs.LG",
"cs.CY"
] | Fairness in machine learning (ML) has garnered significant attention in recent years. While existing research has predominantly focused on the distributive fairness of ML models, there has been limited exploration of procedural fairness. This paper proposes a novel method to achieve procedural fairness during the model... | {
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2501.06756 | Generative AI Enabled Robust Sensor Placement in Cyber-Physical Power
Systems: A Graph Diffusion Approach | [
"eess.SY",
"cs.SY"
] | With advancements in physical power systems and network technologies, integrated Cyber-Physical Power Systems (CPPS) have significantly enhanced system monitoring and control efficiency and reliability. This integration, however, introduces complex challenges in designing coherent CPPS, particularly as few studies conc... | {
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2501.06760 | Metaprism Design for Wireless Communications: Angle-Frequency Analysis,
Physical Realizability Constraints, and Performance Optimization | [
"cs.IT",
"math.IT"
] | Recent advancements in smart radio environment technologies aim to enhance wireless network performance through the use of low-cost electromagnetic (EM) devices. Among these, reconfigurable intelligent surfaces (RIS) have garnered attention for their ability to modify incident waves via programmable scattering elements... | {
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2501.06761 | VidChain: Chain-of-Tasks with Metric-based Direct Preference
Optimization for Dense Video Captioning | [
"cs.CV"
] | Despite the advancements of Video Large Language Models (VideoLLMs) in various tasks, they struggle with fine-grained temporal understanding, such as Dense Video Captioning (DVC). DVC is a complicated task of describing all events within a video while also temporally localizing them, which integrates multiple fine-grai... | {
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2501.06762 | Improving the adaptive and continuous learning capabilities of
artificial neural networks: Lessons from multi-neuromodulatory dynamics | [
"q-bio.NC",
"cs.LG",
"cs.NE"
] | Continuous, adaptive learning-the ability to adapt to the environment and improve performance-is a hallmark of both natural and artificial intelligence. Biological organisms excel in acquiring, transferring, and retaining knowledge while adapting to dynamic environments, making them a rich source of inspiration for art... | {
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2501.06764 | MTPareto: A MultiModal Targeted Pareto Framework for Fake News Detection | [
"cs.LG"
] | Multimodal fake news detection is essential for maintaining the authenticity of Internet multimedia information. Significant differences in form and content of multimodal information lead to intensified optimization conflicts, hindering effective model training as well as reducing the effectiveness of existing fusion m... | {
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2501.06766 | On the Complexity of Global Necessary Reasons to Explain Classification | [
"cs.AI"
] | Explainable AI has garnered considerable attention in recent years, as understanding the reasons behind decisions or predictions made by AI systems is crucial for their successful adoption. Explaining classifiers' behavior is one prominent problem. Work in this area has proposed notions of both local and global explana... | {
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2501.06769 | ODPG: Outfitting Diffusion with Pose Guided Condition | [
"cs.CV"
] | Virtual Try-On (VTON) technology allows users to visualize how clothes would look on them without physically trying them on, gaining traction with the rise of digitalization and online shopping. Traditional VTON methods, often using Generative Adversarial Networks (GANs) and Diffusion models, face challenges in achievi... | {
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2501.06770 | SuperNeRF-GAN: A Universal 3D-Consistent Super-Resolution Framework for
Efficient and Enhanced 3D-Aware Image Synthesis | [
"cs.CV"
] | Neural volume rendering techniques, such as NeRF, have revolutionized 3D-aware image synthesis by enabling the generation of images of a single scene or object from various camera poses. However, the high computational cost of NeRF presents challenges for synthesizing high-resolution (HR) images. Most existing methods ... | {
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2501.06773 | Pareto Set Learning for Multi-Objective Reinforcement Learning | [
"cs.LG"
] | Multi-objective decision-making problems have emerged in numerous real-world scenarios, such as video games, navigation and robotics. Considering the clear advantages of Reinforcement Learning (RL) in optimizing decision-making processes, researchers have delved into the development of Multi-Objective RL (MORL) methods... | {
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2501.06775 | Hierarchy-Boosted Funnel Learning for Identifying Semiconductors with
Ultralow Lattice Thermal Conductivity | [
"cond-mat.mtrl-sci",
"cs.LG"
] | Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relation... | {
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2501.06780 | COMPASS: A Compiler Framework for Resource-Constrained Crossbar-Array
Based In-Memory Deep Learning Accelerators | [
"cs.AR",
"cs.DC",
"cs.ET",
"cs.LG",
"cs.PL"
] | Recently, crossbar array based in-memory accelerators have been gaining interest due to their high throughput and energy efficiency. While software and compiler support for the in-memory accelerators has also been introduced, they are currently limited to the case where all weights are assumed to be on-chip. This limit... | {
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2501.06781 | Eliza: A Web3 friendly AI Agent Operating System | [
"cs.AI"
] | AI Agent, powered by large language models (LLMs) as its cognitive core, is an intelligent agentic system capable of autonomously controlling and determining the execution paths under user's instructions. With the burst of capabilities of LLMs and various plugins, such as RAG, text-to-image/video/3D, etc., the potentia... | {
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2501.06783 | Cost-Effective Robotic Handwriting System with AI Integration | [
"cs.RO",
"cs.AI",
"cs.SY",
"eess.SY"
] | This paper introduces a cost-effective robotic handwriting system designed to replicate human-like handwriting with high precision. Combining a Raspberry Pi Pico microcontroller, 3D-printed components, and a machine learning-based handwriting generation model implemented via TensorFlow, the system converts user-supplie... | {
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2501.06785 | 3DCoMPaT200: Language-Grounded Compositional Understanding of Parts and
Materials of 3D Shapes | [
"cs.CV",
"cs.CL"
] | Understanding objects in 3D at the part level is essential for humans and robots to navigate and interact with the environment. Current datasets for part-level 3D object understanding encompass a limited range of categories. For instance, the ShapeNet-Part and PartNet datasets only include 16, and 24 object categories ... | {
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2501.06786 | Temporal-Aware Spiking Transformer Hashing Based on 3D-DWT | [
"cs.CV"
] | With the rapid growth of dynamic vision sensor (DVS) data, constructing a low-energy, efficient data retrieval system has become an urgent task. Hash learning is one of the most important retrieval technologies which can keep the distance between hash codes consistent with the distance between DVS data. As spiking neur... | {
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2501.06787 | Improving Pain Classification using Spatio-Temporal Deep Learning
Approaches with Facial Expressions | [
"cs.CV",
"cs.AI"
] | Pain management and severity detection are crucial for effective treatment, yet traditional self-reporting methods are subjective and may be unsuitable for non-verbal individuals (people with limited speaking skills). To address this limitation, we explore automated pain detection using facial expressions. Our study le... | {
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2501.06793 | Differentially Private Gradient-Tracking-Based Distributed Stochastic
Optimization over Directed Graphs | [
"eess.SY",
"cs.SY"
] | This paper proposes a new differentially private gradient-tracking-based distributed stochastic optimization algorithm over directed graphs. Specifically, privacy noises are added to each agent's state and tracking variable to prevent information leakage, and then perturbed states and tracking variables are transmitted... | {
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2501.06795 | Bridging the Fairness Gap: Enhancing Pre-trained Models with
LLM-Generated Sentences | [
"cs.CL",
"cs.AI"
] | Pre-trained language models (PLMs) are trained on data that inherently contains gender biases, leading to undesirable impacts. Traditional debiasing methods often rely on external corpora, which may lack quality, diversity, or demographic balance, affecting the effectiveness of debiasing. With the rise of large languag... | {
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2501.06801 | Optimizing Sequencing Coverage Depth in DNA Storage: Insights From DNA
Storage Data | [
"cs.IT",
"math.IT"
] | DNA data storage is now being considered as a new archival storage method for its durability and high information density, but still facing some challenges like high costs and low throughput. By reducing sequencing sample size for decoding digital data, minimizing DNA coverage depth helps lower both costs and system la... | {
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2501.06802 | Unifying Two Types of Scaling Laws from the Perspective of Conditional
Kolmogorov Complexity | [
"cs.AI"
] | In 2020, OpenAI proposed the first type of Scaling Laws, describing the relationships between model loss and the scale of parameters, data, and training computation. In 2024, OpenAI proposed the second type of Scaling Laws, describing the relationship between model inference performance and inference computation. In th... | {
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2501.06805 | A Pan-cancer Classification Model using Multi-view Feature Selection
Method and Ensemble Classifier | [
"cs.LG",
"q-bio.GN"
] | Accurately identifying cancer samples is crucial for precise diagnosis and effective patient treatment. Traditional methods falter with high-dimensional and high feature-to-sample count ratios, which are critical for classifying cancer samples. This study aims to develop a novel feature selection framework specifically... | {
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2501.06806 | Soft Vision-Based Tactile-Enabled SixthFinger: Advancing Daily Objects
Manipulation for Stroke Survivors | [
"cs.RO"
] | The presence of post-stroke grasping deficiencies highlights the critical need for the development and implementation of advanced compensatory strategies. This paper introduces a novel system to aid chronic stroke survivors through the development of a soft, vision-based, tactile-enabled extra robotic finger. By incorp... | {
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2501.06808 | Semantic-CD: Remote Sensing Image Semantic Change Detection towards
Open-vocabulary Setting | [
"cs.CV"
] | Remote sensing image semantic change detection is a method used to analyze remote sensing images, aiming to identify areas of change as well as categorize these changes within images of the same location taken at different times. Traditional change detection methods often face challenges in generalizing across semantic... | {
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2501.06809 | RSRefSeg: Referring Remote Sensing Image Segmentation with Foundation
Models | [
"cs.CV"
] | Referring remote sensing image segmentation is crucial for achieving fine-grained visual understanding through free-format textual input, enabling enhanced scene and object extraction in remote sensing applications. Current research primarily utilizes pre-trained language models to encode textual descriptions and align... | {
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2501.06810 | Improving Cross-Lingual Phonetic Representation of Low-Resource
Languages Through Language Similarity Analysis | [
"eess.AS",
"cs.CL",
"cs.SD"
] | This paper examines how linguistic similarity affects cross-lingual phonetic representation in speech processing for low-resource languages, emphasizing effective source language selection. Previous cross-lingual research has used various source languages to enhance performance for the target low-resource language with... | {
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2501.06813 | Pareto Optimization with Robust Evaluation for Noisy Subset Selection | [
"cs.NE"
] | Subset selection is a fundamental problem in combinatorial optimization, which has a wide range of applications such as influence maximization and sparse regression. The goal is to select a subset of limited size from a ground set in order to maximize a given objective function. However, the evaluation of the objective... | {
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2501.06818 | UR2P-Dehaze: Learning a Simple Image Dehaze Enhancer via Unpaired Rich
Physical Prior | [
"cs.CV"
] | Image dehazing techniques aim to enhance contrast and restore details, which are essential for preserving visual information and improving image processing accuracy. Existing methods rely on a single manual prior, which cannot effectively reveal image details. To overcome this limitation, we propose an unpaired image d... | {
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2501.06819 | A Study on Educational Data Analysis and Personalized Feedback Report
Generation Based on Tags and ChatGPT | [
"cs.AI"
] | This study introduces a novel method that employs tag annotation coupled with the ChatGPT language model to analyze student learning behaviors and generate personalized feedback. Central to this approach is the conversion of complex student data into an extensive set of tags, which are then decoded through tailored pro... | {
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2501.06823 | MEXA-CTP: Mode Experts Cross-Attention for Clinical Trial Outcome
Prediction | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | Clinical trials are the gold standard for assessing the effectiveness and safety of drugs for treating diseases. Given the vast design space of drug molecules, elevated financial cost, and multi-year timeline of these trials, research on clinical trial outcome prediction has gained immense traction. Accurate prediction... | {
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2501.06825 | Event Argument Extraction with Enriched Prompts | [
"cs.CL"
] | This work aims to delve deeper into prompt-based event argument extraction (EAE) models. We explore the impact of incorporating various types of information into the prompt on model performance, including trigger, other role arguments for the same event, and role arguments across multiple events within the same documen... | {
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2501.06826 | Correcting Annotator Bias in Training Data: Population-Aligned Instance
Replication (PAIR) | [
"stat.ME",
"cs.CL"
] | Models trained on crowdsourced labels may not reflect broader population views when annotator pools are not representative. Since collecting representative labels is challenging, we propose Population-Aligned Instance Replication (PAIR), a method to address this bias through statistical adjustment. Using a simulation s... | {
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2501.06827 | Leveraging Taxonomy and LLMs for Improved Multimodal Hierarchical
Classification | [
"cs.AI"
] | Multi-level Hierarchical Classification (MLHC) tackles the challenge of categorizing items within a complex, multi-layered class structure. However, traditional MLHC classifiers often rely on a backbone model with independent output layers, which tend to ignore the hierarchical relationships between classes. This overs... | {
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2501.06828 | GeoPix: Multi-Modal Large Language Model for Pixel-level Image
Understanding in Remote Sensing | [
"cs.CV"
] | Multi-modal large language models (MLLMs) have achieved remarkable success in image- and region-level remote sensing (RS) image understanding tasks, such as image captioning, visual question answering, and visual grounding. However, existing RS MLLMs lack the pixel-level dialogue capability, which involves responding t... | {
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2501.06831 | Towards Counterfactual and Contrastive Explainability and Transparency
of DCNN Image Classifiers | [
"cs.CV",
"cs.AI"
] | Explainability of deep convolutional neural networks (DCNNs) is an important research topic that tries to uncover the reasons behind a DCNN model's decisions and improve their understanding and reliability in high-risk environments. In this regard, we propose a novel method for generating interpretable counterfactual a... | {
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2501.06832 | A novel multi-agent dynamic portfolio optimization learning system based
on hierarchical deep reinforcement learning | [
"cs.LG",
"cs.MA"
] | Deep Reinforcement Learning (DRL) has been extensively used to address portfolio optimization problems. The DRL agents acquire knowledge and make decisions through unsupervised interactions with their environment without requiring explicit knowledge of the joint dynamics of portfolio assets. Among these DRL algorithms,... | {
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2501.06833 | Unveiling Temporal Trends in 19th Century Literature: An Information
Retrieval Approach | [
"cs.DL",
"cs.IR"
] | In English literature, the 19th century witnessed a significant transition in styles, themes, and genres. Consequently, the novels from this period display remarkable diversity. This paper explores these variations by examining the evolution of term usage in 19th century English novels through the lens of information r... | {
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2501.06834 | LLMs Model Non-WEIRD Populations: Experiments with Synthetic Cultural
Agents | [
"cs.AI",
"cs.CL",
"econ.GN",
"q-fin.EC"
] | Despite its importance, studying economic behavior across diverse, non-WEIRD (Western, Educated, Industrialized, Rich, and Democratic) populations presents significant challenges. We address this issue by introducing a novel methodology that uses Large Language Models (LLMs) to create synthetic cultural agents (SCAs) r... | {
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2501.06835 | X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding | [
"cs.CV"
] | Long-form egocentric video understanding provides rich contextual information and unique insights into long-term human behaviors, holding significant potential for applications in embodied intelligence, long-term activity analysis, and personalized assistive technologies. However, existing benchmark datasets primarily ... | {
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2501.06836 | SAM-DA: Decoder Adapter for Efficient Medical Domain Adaptation | [
"cs.CV"
] | This paper addresses the domain adaptation challenge for semantic segmentation in medical imaging. Despite the impressive performance of recent foundational segmentation models like SAM on natural images, they struggle with medical domain images. Beyond this, recent approaches that perform end-to-end fine-tuning of mod... | {
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2501.06837 | An efficient approach to represent enterprise web application structure
using Large Language Model in the service of Intelligent Quality Engineering | [
"cs.AI",
"cs.SE"
] | This paper presents a novel approach to represent enterprise web application structures using Large Language Models (LLMs) to enable intelligent quality engineering at scale. We introduce a hierarchical representation methodology that optimizes the few-shot learning capabilities of LLMs while preserving the complex rel... | {
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2501.06838 | Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale
Super-Resolution | [
"eess.IV",
"cs.CV"
] | Equipped with the continuous representation capability of Multi-Layer Perceptron (MLP), Implicit Neural Representation (INR) has been successfully employed for Arbitrary-scale Super-Resolution (ASR). However, the limited receptive field of the linear layers in MLP restricts the representation capability of INR, while i... | {
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2501.06841 | Faithful Counterfactual Visual Explanations (FCVE) | [
"cs.CV"
] | Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations ... | {
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2501.06842 | SPAM: Spike-Aware Adam with Momentum Reset for Stable LLM Training | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large Language Models (LLMs) have demonstrated exceptional performance across diverse tasks, yet their training remains highly resource-intensive and susceptible to critical challenges such as training instability. A predominant source of this instability stems from gradient and loss spikes, which disrupt the learning ... | {
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2501.06843 | Leveraging the Global Research Infrastructure to Characterize the Impact
of National Science Foundation Research | [
"cs.DL",
"cs.SI"
] | The Global Research infrastructure (GRI) is made up of the repositories and organizations that provide persistent identifiers (PIDs) and metadata for many kinds of research objects and connect these objects to funders, research institutions, researchers, and one another using PIDs. The INFORMATE Project has combined th... | {
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2501.06847 | Accelerating Discovery in Natural Science Laboratories with AI and
Robotics: Perspectives and Challenges from the 2024 IEEE ICRA Workshop,
Yokohama, Japan | [
"cs.RO"
] | Science laboratory automation enables accelerated discovery in life sciences and materials. However, it requires interdisciplinary collaboration to address challenges such as robust and flexible autonomy, reproducibility, throughput, standardization, the role of human scientists, and ethics. This article highlights the... | {
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2501.06848 | A General Framework for Inference-time Scaling and Steering of Diffusion
Models | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Diffusion models produce impressive results in modalities ranging from images and video to protein design and text. However, generating samples with user-specified properties remains a challenge. Recent research proposes fine-tuning models to maximize rewards that capture desired properties, but these methods require e... | {
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2501.06857 | What Is a Counterfactual Cause in Action Theories? | [
"cs.AI"
] | Since the proposal by Halpern and Pearl, reasoning about actual causality has gained increasing attention in artificial intelligence, ranging from domains such as model-checking and verification to reasoning about actions and knowledge. More recently, Batusov and Soutchanski proposed a notion of actual achievement caus... | {
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2501.06859 | A Comprehensive Evaluation of Large Language Models on Mental Illnesses
in Arabic Context | [
"cs.CL",
"cs.AI"
] | Mental health disorders pose a growing public health concern in the Arab world, emphasizing the need for accessible diagnostic and intervention tools. Large language models (LLMs) offer a promising approach, but their application in Arabic contexts faces challenges including limited labeled datasets, linguistic complex... | {
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2501.06862 | LarvSeg: Exploring Image Classification Data For Large Vocabulary
Semantic Segmentation via Category-wise Attentive Classifier | [
"cs.CV",
"cs.AI"
] | Scaling up the vocabulary of semantic segmentation models is extremely challenging because annotating large-scale mask labels is labour-intensive and time-consuming. Recently, language-guided segmentation models have been proposed to address this challenge. However, their performance drops significantly when applied to... | {
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2501.06863 | Transfer Learning of Tabular Data by Finetuning Large Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Despite the artificial intelligence (AI) revolution, deep learning has yet to achieve much success with tabular data due to heterogeneous feature space and limited sample sizes without viable transfer learning. The new era of generative AI, powered by large language models (LLM), brings unprecedented learning opportuni... | {
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2501.06867 | Toward a Universal Concept of Artificial Personality: Implementing
Robotic Personality in a Kinova Arm | [
"cs.RO",
"cs.HC"
] | The fundamental role of personality in shaping interactions is increasingly being exploited in robotics. A carefully designed robotic personality has been shown to improve several key aspects of Human-Robot Interaction (HRI). However, the fragmentation and rigidity of existing approaches reveal even greater challenges ... | {
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2501.06868 | Variable Selection Methods for Multivariate, Functional, and Complex
Biomedical Data in the AI Age | [
"stat.ML",
"cs.LG",
"stat.AP",
"stat.ME"
] | Many problems within personalized medicine and digital health rely on the analysis of continuous-time functional biomarkers and other complex data structures emerging from high-resolution patient monitoring. In this context, this work proposes new optimization-based variable selection methods for multivariate, function... | {
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2501.06869 | A Foundational Generative Model for Breast Ultrasound Image Analysis | [
"cs.AI",
"cs.CV",
"cs.HC",
"cs.LG"
] | Foundational models have emerged as powerful tools for addressing various tasks in clinical settings. However, their potential development to breast ultrasound analysis remains untapped. In this paper, we present BUSGen, the first foundational generative model specifically designed for breast ultrasound image analysis.... | {
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2501.06873 | Causal Claims in Economics | [
"econ.GN",
"cs.CL",
"cs.IR",
"cs.SI",
"q-fin.EC",
"stat.ME"
] | We analyze over 44,000 NBER and CEPR working papers from 1980 to 2023 using a custom language model to construct knowledge graphs that map economic concepts and their relationships. We distinguish between general claims and those documented via causal inference methods (e.g., DiD, IV, RDD, RCTs). We document a substant... | {
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2501.06878 | Uncertainty-Aware Online Extrinsic Calibration: A Conformal Prediction
Approach | [
"cs.CV"
] | Accurate sensor calibration is crucial for autonomous systems, yet its uncertainty quantification remains underexplored. We present the first approach to integrate uncertainty awareness into online extrinsic calibration, combining Monte Carlo Dropout with Conformal Prediction to generate prediction intervals with a gua... | {
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2501.06879 | Defect Detection Network In PCB Circuit Devices Based on GAN Enhanced
YOLOv11 | [
"cs.CE",
"cs.AI",
"cs.CV"
] | This study proposes an advanced method for surface defect detection in printed circuit boards (PCBs) using an improved YOLOv11 model enhanced with a generative adversarial network (GAN). The approach focuses on identifying six common defect types: missing hole, rat bite, open circuit, short circuit, burr, and virtual w... | {
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2501.06880 | Real-Time Neural-Enhancement for Online Cloud Gaming | [
"cs.NI",
"cs.CV"
] | Online Cloud gaming demands real-time, high-quality video transmission across variable wide-area networks (WANs). Neural-enhanced video transmission algorithms employing super-resolution (SR) for video quality enhancement have effectively challenged WAN environments. However, these SR-based methods require intensive fi... | {
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2501.06884 | Transforming Vision Transformer: Towards Efficient Multi-Task
Asynchronous Learning | [
"cs.CV"
] | Multi-Task Learning (MTL) for Vision Transformer aims at enhancing the model capability by tackling multiple tasks simultaneously. Most recent works have predominantly focused on designing Mixture-of-Experts (MoE) structures and in tegrating Low-Rank Adaptation (LoRA) to efficiently perform multi-task learning. However... | {
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2501.06887 | MedGrad E-CLIP: Enhancing Trust and Transparency in AI-Driven Skin
Lesion Diagnosis | [
"cs.CV",
"cs.AI",
"cs.ET",
"cs.LG"
] | As deep learning models gain attraction in medical data, ensuring transparent and trustworthy decision-making is essential. In skin cancer diagnosis, while advancements in lesion detection and classification have improved accuracy, the black-box nature of these methods poses challenges in understanding their decision p... | {
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2501.06892 | Language Fusion for Parameter-Efficient Cross-lingual Transfer | [
"cs.CL"
] | Limited availability of multilingual text corpora for training language models often leads to poor performance on downstream tasks due to undertrained representation spaces for languages other than English. This 'under-representation' has motivated recent cross-lingual transfer methods to leverage the English represent... | {
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2501.06896 | Introduction to the Usage of Open Data from the Large Hadron Collider
for Computer Scientists in the Context of Machine Learning | [
"cs.LG",
"hep-ex",
"physics.data-an"
] | Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage the latest developments in computer science for particle physics, a strengthened collaboration between computer scientists and physicists is ... | {
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2501.06897 | ActiveGAMER: Active GAussian Mapping through Efficient Rendering | [
"cs.CV",
"cs.RO"
] | We introduce ActiveGAMER, an active mapping system that utilizes 3D Gaussian Splatting (3DGS) to achieve high-quality, real-time scene mapping and exploration. Unlike traditional NeRF-based methods, which are computationally demanding and restrict active mapping performance, our approach leverages the efficient renderi... | {
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2501.06903 | Synthetic Prior for Few-Shot Drivable Head Avatar Inversion | [
"cs.CV"
] | We present SynShot, a novel method for the few-shot inversion of a drivable head avatar based on a synthetic prior. We tackle two major challenges. First, training a controllable 3D generative network requires a large number of diverse sequences, for which pairs of images and high-quality tracked meshes are not always ... | {
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2501.06904 | From Simulation to Field: Learning Terrain Traversability for Real-World
Deployment | [
"cs.RO"
] | The challenge of traversability estimation is a crucial aspect of autonomous navigation in unstructured outdoor environments such as forests. It involves determining whether certain areas are passable or risky for robots, taking into account factors like terrain irregularities, slopes, and potential obstacles. The majo... | {
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2501.06907 | Deep Learning and Foundation Models for Weather Prediction: A Survey | [
"cs.LG"
] | Physics-based numerical models have been the bedrock of atmospheric sciences for decades, offering robust solutions but often at the cost of significant computational resources. Deep learning (DL) models have emerged as powerful tools in meteorology, capable of analyzing complex weather and climate data by learning int... | {
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2501.06909 | Local Foreground Selection aware Attentive Feature Reconstruction for
few-shot fine-grained plant species classification | [
"cs.CV"
] | Plant species exhibit significant intra-class variation and minimal inter-class variation. To enhance classification accuracy, it is essential to reduce intra-class variation while maximizing inter-class variation. This paper addresses plant species classification using a limited number of labelled samples and introduc... | {
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2501.06910 | A General Framework for Error-controlled Unstructured Scientific Data
Compression | [
"cs.IT",
"math.IT"
] | Data compression plays a key role in reducing storage and I/O costs. Traditional lossy methods primarily target data on rectilinear grids and cannot leverage the spatial coherence in unstructured mesh data, leading to suboptimal compression ratios. We present a multi-component, error-bounded compression framework desig... | {
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2501.06911 | Risk-Averse Finetuning of Large Language Models | [
"cs.AI",
"cs.CL"
] | We consider the challenge of mitigating the generation of negative or toxic content by the Large Language Models (LLMs) in response to certain prompts. We propose integrating risk-averse principles into LLM fine-tuning to minimize the occurrence of harmful outputs, particularly rare but significant events. By optimizin... | {
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2501.06916 | Black-box optimization and quantum annealing for filtering out
mislabeled training instances | [
"cs.LG",
"cond-mat.stat-mech",
"quant-ph"
] | This study proposes an approach for removing mislabeled instances from contaminated training datasets by combining surrogate model-based black-box optimization (BBO) with postprocessing and quantum annealing. Mislabeled training instances, a common issue in real-world datasets, often degrade model generalization, neces... | {
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2501.06917 | Optimizing Phase Allocation in Unbalanced Power Distribution Networks
using a Linearized DistFlow Formulation | [
"eess.SY",
"cs.SY"
] | Power distribution networks, especially in North America, are often unbalanced but are designed to keep unbalance levels within the limits specified by IEEE, IEC, and NEMA standards. However, rapid integration of unbalanced devices, such as electric vehicle (EV) chargers and single-phase solar plants, can exacerbate th... | {
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2501.06918 | Driver Age and Its Effect on Key Driving Metrics: Insights from Dynamic
Vehicle Data | [
"stat.ME",
"cs.CV"
] | By 2030, the senior population aged 65 and older is expected to increase by over 50%, significantly raising the number of older drivers on the road. Drivers over 70 face higher crash death rates compared to those in their forties and fifties, underscoring the importance of developing more effective safety interventions... | {
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2501.06919 | Shake-VLA: Vision-Language-Action Model-Based System for Bimanual
Robotic Manipulations and Liquid Mixing | [
"cs.RO"
] | This paper introduces Shake-VLA, a Vision-Language-Action (VLA) model-based system designed to enable bimanual robotic manipulation for automated cocktail preparation. The system integrates a vision module for detecting ingredient bottles and reading labels, a speech-to-text module for interpreting user commands, and a... | {
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2501.06922 | Benchmarking YOLOv8 for Optimal Crack Detection in Civil Infrastructure | [
"cs.CV"
] | Ensuring the structural integrity and safety of bridges is crucial for the reliability of transportation networks and public safety. Traditional crack detection methods are increasingly being supplemented or replaced by advanced artificial intelligence (AI) techniques. However, most of the models rely on two-stage targ... | {
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2501.06923 | Optimal Online Bookmaking for Binary Games | [
"cs.GT",
"cs.IT",
"cs.LG",
"math.IT",
"math.OC"
] | In online betting, the bookmaker can update the payoffs it offers on a particular event many times before the event takes place, and the updated payoffs may depend on the bets accumulated thus far. We study the problem of bookmaking with the goal of maximizing the return in the worst-case, with respect to the gamblers'... | {
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2501.06925 | A Hybrid Virtual Element Method and Deep Learning Approach for Solving
One-Dimensional Euler-Bernoulli Beams | [
"cs.LG"
] | A hybrid framework integrating the Virtual Element Method (VEM) with deep learning is presented as an initial step toward developing efficient and flexible numerical models for one-dimensional Euler-Bernoulli beams. The primary aim is to explore a data-driven surrogate model capable of predicting displacement fields ac... | {
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2501.06926 | Automatic Double Reinforcement Learning in Semiparametric Markov
Decision Processes with Applications to Long-Term Causal Inference | [
"stat.ML",
"cs.LG",
"stat.ME"
] | Double reinforcement learning (DRL) enables statistically efficient inference on the value of a policy in a nonparametric Markov Decision Process (MDP) given trajectories generated by another policy. However, this approach necessarily requires stringent overlap between the state distributions, which is often violated i... | {
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2501.06927 | CULTURE3D: Cultural Landmarks and Terrain Dataset for 3D Applications | [
"cs.CV"
] | In this paper, we present a large-scale fine-grained dataset using high-resolution images captured from locations worldwide. Compared to existing datasets, our dataset offers a significantly larger size and includes a higher level of detail, making it uniquely suited for fine-grained 3D applications. Notably, our datas... | {
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2501.06929 | Why are we living the age of AI applications right now? The long
innovation path from AI's birth to a child's bedtime magic | [
"cs.CY",
"cs.AI"
] | Today a four-year-old child who does not know how to read or write can now create bedtime stories with graphical illustrations and narrated audio, using AI tools that seamlessly transform speech into text, generate visuals, and convert text back into speech in a natural and engaging manner. This remarkable example demo... | {
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2501.06932 | Harnessing Large Language Models for Disaster Management: A Survey | [
"cs.CL",
"cs.CY",
"cs.LG"
] | Large language models (LLMs) have revolutionized scientific research with their exceptional capabilities and transformed various fields. Among their practical applications, LLMs have been playing a crucial role in mitigating threats to human life, infrastructure, and the environment. Despite growing research in disaste... | {
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} |
2501.06933 | Neural equilibria for long-term prediction of nonlinear conservation
laws | [
"cs.LG",
"physics.comp-ph",
"physics.flu-dyn"
] | We introduce Neural Discrete Equilibrium (NeurDE), a machine learning (ML) approach for long-term forecasting of flow phenomena that relies on a "lifting" of physical conservation laws into the framework of kinetic theory. The kinetic formulation provides an excellent structure for ML algorithms by separating nonlinear... | {
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2501.06934 | A group-theoretic framework for machine learning in hyperbolic spaces | [
"cs.LG"
] | Embedding the data in hyperbolic spaces can preserve complex relationships in very few dimensions, thus enabling compact models and improving efficiency of machine learning (ML) algorithms. The underlying idea is that hyperbolic representations can prevent the loss of important structural information for certain ubiqui... | {
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2501.06937 | An Empirical Study of Deep Reinforcement Learning in Continuing Tasks | [
"cs.AI"
] | In reinforcement learning (RL), continuing tasks refer to tasks where the agent-environment interaction is ongoing and can not be broken down into episodes. These tasks are suitable when environment resets are unavailable, agent-controlled, or predefined but where all rewards-including those beyond resets-are critical.... | {
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2501.06938 | Evaluating unsupervised contrastive learning framework for MRI sequences
classification | [
"cs.CV",
"eess.IV"
] | The automatic identification of Magnetic Resonance Imaging (MRI) sequences can streamline clinical workflows by reducing the time radiologists spend manually sorting and identifying sequences, thereby enabling faster diagnosis and treatment planning for patients. However, the lack of standardization in the parameters o... | {
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2501.06939 | Super-Resolution of 3D Micro-CT Images Using Generative Adversarial
Networks: Enhancing Resolution and Segmentation Accuracy | [
"eess.IV",
"cs.CV",
"cs.LG"
] | We develop a procedure for substantially improving the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks with a Machine Learning (ML) Generative Model. The proposed model enhances the resolution eightfold (8x) and addresses segmentation inaccuracies due to the overlapping X-ray attenuation in... | {
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} |
2501.06940 | Collaborative Human Activity Recognition with Passive Inter-Body
Electrostatic Field | [
"eess.SY",
"cs.SY"
] | The passive body-area electrostatic field has recently been aspiringly explored for wearable motion sensing, harnessing its two thrilling characteristics: full-body motion sensitivity and environmental sensitivity, which potentially empowers human activity recognition both independently and jointly from a single sensin... | {
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} |
2501.06942 | Comparison of Autoencoders for tokenization of ASL datasets | [
"cs.LG",
"cs.CV"
] | Generative AI, powered by large language models (LLMs), has revolutionized applications across text, audio, images, and video. This study focuses on developing and evaluating encoder-decoder architectures for the American Sign Language (ASL) image dataset, consisting of 87,000 images across 29 hand sign classes. Three ... | {
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} |
2501.06946 | Learning Implicit Social Navigation Behavior using Deep Inverse
Reinforcement Learning | [
"cs.RO"
] | This paper reports on learning a reward map for social navigation in dynamic environments where the robot can reason about its path at any time, given agents' trajectories and scene geometry. Humans navigating in dense and dynamic indoor environments often work with several implied social rules. A rule-based approach f... | {
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